Frederic t
Founder

Timeseer offers an AI‑driven industrial data validation platform that detects anomalies, validates sensor data fitness, and provides root‑cause analysis for millions of IIoT devices. The solution automates data cleaning, correction, and audit‑ready reporting, enabling reliable inputs for analytics, digital twins, billing, and regulatory compliance.
Raised to date
$6MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.




Founder
Industrial operations rely on massive streams of sensor data, but drifted, flatlined, or missing readings introduce “data downtime” that erodes trust, leads to inaccurate billing, regulatory non‑compliance, and sub‑optimal decision making.
Timeseer provides an AI‑driven industrial data validation platform that detects anomalies, validates data fitness, and resolves issues across millions of IIoT devices. The system automatically flags spikes, drifts, flatlines and other artefacts using over 100 built‑in checks and machine‑learning models that learn from each customer’s data. Detected incidents are grouped and enriched with root‑cause analysis, enabling teams to clean data manually, apply automated correction, or trigger sensor maintenance. Validated, fit‑for‑purpose data can then be streamed to downstream analytics, digital twins, billing systems, or regulatory reporting pipelines via APIs, SDKs, or out‑of‑the‑box integrations. The platform supports on‑prem, cloud, or SaaS deployments and includes auditability, role‑based access, and single sign‑on for enterprise governance.
Primary customers are industrial data engineers, digital twin teams, and operations managers in utilities, process manufacturing, energy trading, and regulated sectors that need reliable sensor data for analytics, billing, and compliance.